Current deep learning techniques such as YOLO (You Only Look Once) [1]} perform object localization tasks (bounding box estimation) very successfully. It seems intuitive that a similar deep neural network should be able perform object localization with complicated shapes rather than rectangular bounding boxes, solving the instance segmentation problem accurately and efficiently. In this section, we discuss previous approaches to CNN-based instance segmentation and the idea behind the YOLO object localization algorithm before explaining how we adapt it to the task of instance segmentation via instance parameterization.
